Staging Epileptogenesis with Deep Neural Networks

TL;DR

This paper introduces a deep neural network approach to stage epileptogenesis using EEG data, enabling early detection of disease progression phases before spontaneous seizures occur.

Contribution

It presents the first successful method to classify different phases of epileptogenesis prior to spontaneous seizures using deep learning on EEG signals.

Findings

Achieved high classification accuracy with AUCs of 0.93, 0.89, and 0.86 for different phases.

Identified potential EEG biomarkers for staging epileptogenesis.

Demonstrated early detection of epileptogenic phases before spontaneous seizures.

Abstract

Epilepsy is a common neurological disorder characterized by recurrent seizures accompanied by excessive synchronous brain activity. The process of structural and functional brain alterations leading to increased seizure susceptibility and eventually spontaneous seizures is called epileptogenesis (EPG) and can span months or even years. Detecting and monitoring the progression of EPG could allow for targeted early interventions that could slow down disease progression or even halt its development. Here, we propose an approach for staging EPG using deep neural networks and identify potential electroencephalography (EEG) biomarkers to distinguish different phases of EPG. Specifically, continuous intracranial EEG recordings were collected from a rodent model where epilepsy is induced by electrical perforant pathway stimulation (PPS). A deep neural network (DNN) is trained to distinguish EEG signals from before stimulation (baseline), shortly after the PPS and long after the PPS but before the first spontaneous seizure (FSS). Experimental results show that our proposed method can classify EEG signals from the three phases with an average area under the curve (AUC) of 0.93, 0.89, and 0.86. To the best of our knowledge, this represents the first successful attempt to stage EPG prior to the FSS using DNNs.